Numerous countries throughout the world are facing an unprecedented rise in healthcare costs affecting both healthcare providers and employers. One major component of healthcare costs is costs associated with surgery. Another component of healthcare costs is costs associated with diagnostics.
Healthcare predictive models have been employed that utilize actuarial models of cost predictions based on standard demographic data of patients to predict health care costs. Predictive statistical modeling is a field of data mining that utilizes statistics, machine learning, pattern recognition, and other techniques to analyze information and/or data. Other healthcare predictive models have included timing data associated with the periods when patients are examined for a particular illness to estimate costs. However, prior attempts at predictive healthcare models have focused on resource utilization, rather than the likelihood that an individual will undergo a specific surgical or diagnostic procedure.
Accordingly, there remains a widespread need for improved mechanisms to assist healthcare providers and employers to lower healthcare costs while providing superior quality of healthcare to patients. For healthcare providers such as health insurers and managed care organizations (“MCOs”), there exists a need for determining which patients are likely to present the highest risk of undergoing a surgical or diagnostic procedure, referred to as event risk, which can assist in developing strategies for managing healthcare programs.